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Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases which is increasingly supported by algorithms based on machine learning. Major obstacles for the development of automatic ECG interpretation algorithms are both the lack of public datasets and well-defined benchmarking procedures to allow comparison s of different algorithms. To address these issues, we put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The ECG-waveform data was annotated by up to two cardiologists as a multi-label dataset, where diagnostic labels were further aggregated into super and subclasses. The dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records. The combination with additional metadata on demographics, additional diagnostic statements, diagnosis likelihoods, manually annotated signal properties as well as suggested folds for splitting training and test sets turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms.Measurement(s)electrocardiography • cardiovascular systemTechnology Type(s)12 lead electrocardiographyFactor Type(s)presence of co-occurring diseasesSample Characteristic - OrganismHomo sapiensMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12098055

Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy. Here, we outline the legal and ethical challenges big data brings to patient privacy. We discuss, among other topics, how best to conceive of health privacy; the importance of equity, consent, and patient governance in data collection; discrimination in data uses; and how to handle data breaches. We close by sketching possible ways forward for the regulatory system.

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule–focused tandem mass spectrometry (MS 2 ) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS 2 dataset and to connect this chemical insight to the user’s underlying biological questions. This can be performed within one liquid chromatography (LC)-MS 2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS 2 data with sample information (metadata) and annotated MS 2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking—one of the main analysis tools used within the GNPS platform—creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS 2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions. Global Natural Product Social Molecular Networking (GNPS) is an online tandem mass spectrometry (MS 2 ) data curation and analysis infrastructure. This protocol describes how to use GNPS to explore uploaded metabolomics data.

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative 1 – 6 , but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 10 7 chemical synapses 7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster 8 , 9 . The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities 10 – 12 . Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome—a map of projections between regions—from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species. FlyWire presents a neuronal wiring diagram of the whole fly brain with annotations for cell types, classes, nerves, hemilineages and predicted neurotransmitters, with data products and an open ecosystem to facilitate exploration and browsing.

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set.Measurement(s)diseases and abnormal findings from chest X-ray scansTechnology Type(s)AI is used to detect diseases and abnormal findingsSample Characteristic - LocationVietnam

The World Checklist of Vascular Plants (WCVP) is a comprehensive list of scientifically described plant species, compiled over four decades, from peer-reviewed literature, authoritative scientific databases, herbaria and observations, then reviewed by experts. It is a vital tool to facilitate plant diversity research, conservation and effective management, including sustainable use and equitable sharing of benefits. To maximise utility, such lists should be accessible, explicitly evidence-based, transparent, expert-reviewed, and regularly updated, incorporating new evidence and emerging scientific consensus. WCVP largely meets these criteria, being continuously updated and freely available online. Users can browse, search, or download a user-defined subset of accepted species with corresponding synonyms and bibliographic details, or a date-stamped full dataset. To facilitate appropriate data reuse by individual researchers and global initiatives including Global Biodiversity Information Facility, Catalogue of Life and World Flora Online, we document data collation and review processes, the underlying data structure, and the international data standards and technical validation that ensure data quality and integrity. We also address the questions most frequently received from users. Measurement(s) Vascular Plant • Species Technology Type(s) digital curation Sample Characteristic - Organism Tracheophyta Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15035046

To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.